Diagnosing Model Performance with Learning Curves

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Learning curves are a widely used diagnostic tool in machine learning for algorithms such as deep learning that learn incrementally. During training time, we evaluate model performance on both the training and hold-out validation dataset and we plot this performance for each training step i. Reviewing learning curves of models during training can be used to diagnose problems with learning, such as an underfit or overfit model, as well as whether the training and validation datasets are suitably representative. In this notebook, I will illustrate to how you can use learning curves to:. This notebook will demonstrate these issues with learning curve plots but does not show any code.

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The learning curve theory is a approach to understand the improved performance of an employee or investment over age. The idea is that the add an employee does something, the advance they will get at it, which translates to lower cost and advanced output in the long term. It's a useful model for tracking advance, improving productivity and ensuring your ballet company is hitting certain performance targets. All the rage this article, we explain what the learning curve theory is, how it works and how you can affect it to improve your company's accomplishment.

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